Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

Atsushi Nitanda

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arXiv · 1506.03016

Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

Atsushi Nitanda

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Abstract

We propose an optimization method for minimizing the finite sums of smooth convex functions. Our method incorporates an accelerated gradient descent (AGD) and a stochastic variance reduction gradient (SVRG) in a mini-batch setting. Unlike SVRG, our method can be directly applied to non-strongly and strongly convex problems. We show that our method achieves a lower overall complexity than the recently proposed methods that supports non-strongly convex problems. Moreover, this method has a fast rate of convergence for strongly convex problems. Our experiments show the effectiveness of our method.

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Atsushi Nitanda. 2015-06-09. Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. https://arxiv.org/abs/1506.03016

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